text_embeddings.py

from cosine_similarity import compute_cosine_similarity
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2")
texts = [
    "The canine barked loudly.",
    "The dog made a noisy bark.",
    "He ate a lot of pizza.",
    "He devoured a large quantity of pizza pie.",
]

text_embeddings = model.encode(texts)

print(type(text_embeddings))

print(text_embeddings.shape)

text_embeddings_dict = dict(zip(texts, list(text_embeddings)))

dog_text_1 = "The canine barked loudly."
dog_text_2 = "The dog made a noisy bark."
print(
    compute_cosine_similarity(
        text_embeddings_dict[dog_text_1], text_embeddings_dict[dog_text_2]
    )
)

pizza_text_1 = "He ate a lot of pizza."
pizza_text_2 = "He devoured a large quantity of pizza pie."
print(
    compute_cosine_similarity(
        text_embeddings_dict[pizza_text_1], text_embeddings_dict[pizza_text_2]
    )
)

print(
    compute_cosine_similarity(
        text_embeddings_dict[dog_text_1], text_embeddings_dict[pizza_text_1]
    )
)